A continuación, te explicamos cómo puedes adquirir las habilidades más demandadas en análisis de datos en la actualidad.

in my day to day analytics work, I invest my 80% effort in SQL to transform the data in the required shape. so to learn SQL is a must. and as per my learning journey, these are the best platforms/sources to learn the same. 1. SQLBolt - start with this website to learn the basic to advanced sql concepts in an interactive way 2. Hackerrank - move ahead and practice more hard challenges on Hackerrank and get the certifications as well to brag on LinkedIn 3. Leetcode - you can further practice with some of the hard challenges here 4. The final step would be to get the open source datasets(eg. Kaggle), load the data in MySQL or BigQuery and then try to transform the data to solve an analytics problem

SQL is one of the most commonly used tools in data. This is especially the case for people with a non-technical background and good understanding of business processes. There are many SQL platforms but the best way to start is with whichever platform is most accessible. This could be in the workplace or by enrolling in a course online. In either case the language of SQL makes logical sense - you SELECT data from a TABLE, some conditions (WHERE/HAVING) might be used and if there are multiple tables you can JOIN them. The only prerequisite would be to have a basic understanding of data i.e you are comfortable with spreadsheets such as excel or google sheets.

Learn sql for structured data. As time passes, working with Sql tools get easier. With new updates, querying becomes an easy task. You can even use the help of AI but it's recommended to learn sql fundamentals first.

To acquire high-demand skills in data analytics, keep abreast of industry trends and focus on key areas like programming (Python, R), data visualization (Tableau, Power BI), and machine learning. Take relevant courses, earn certifications, and work on hands-on projects with real datasets. Join online communities, attend webinars, and network with industry professionals. Continuously improve your analytical and problem-solving skills to remain competitive in the field.

To acquire the most in-demand skills in data analytics today, you should focus on mastering key technical tools such as Python, SQL, and data visualization tools like Power BI. Additionally, gaining expertise in statistical analysis and machine learning algorithms is vital to stay competitive in the field. Hands-on projects and real-world case studies can greatly enhance your practical skills. Lastly, continuous learning and self-improvement are key to mastering data analytics skills. Stay curious, explore new tools and techniques, and never stop challenging yourself to reach new heights in your data analytics journey. the field of data analytics is constantly evolving, so adaptability and a growth mindset are essential for success.

In my experience, focusing on object-oriented programming (OOP) with Python rather than just writing scripts has been particularly beneficial. By organizing code into classes and modules, you can achieve better encapsulation and structure, making it easier to manage complex data projects. This approach has sharpened my Python skills and allowed me to build robust data pipelines that ensure data quality and integrity for the clients I work with. If you’re learning Python, consider diving into OOP—it’s a game-changer for long-term development and maintainability.

Pandas is the python's library for data cleaning. You can simply install this using "pip install pandas" in your terminal. Next you might read some books about just "pandas" and then practice some codes there. That's kinda satisfying.

Python is a most important tool in the data industry, most of the ETL processes are handled in this tool. If we want to grasp each element of Python, we can start with the basic concepts of it such as variables, expressions, operators, iterations, data structure, etc. Once we are familiar with the basics then can move on to the data manipulation libraries like NumPy and Pandas, and we can practice data cleaning using these libraries and we can automate the process using Python and Libraries. After all the practice, now is the time to showcase the result which can be achieved by visualization in Python using Matplotlib, Seaborn, and Plotly. Implement all this using data from Kaggle or any other sources in Jupyter Notebook.

Python (alongside R) is one of the most commonly used advanced analytics languages and tools in the data profession. If SQL is commonly used as a way to source data, Python is where all levels of analysis can be applied such as summary statistics and visualisations to predictive modelling, clustering or fuzzy matching. Python is also a programming language which means it can enable other types of functions such as api calls or even basic game development.

Attend online or offline python meetups or hackathons to network and collaborate with other Python enthusiasts and learn from their experiences.

I've studied mathematics in college but i hadn't realized stats importance back then. Stats is the reality of life. It's that one thing depending on something else(or more things) to happen. If you wanna analyize data you surely want to know this science(i call it reality). You also want to read naked statistics by Charles Wheelan.

Statistical Analysis is very crucial in Machine Learning and Data Cleaning. Focusing on stats can help me get more insight into the data instead of only seeing its distribution. Stats topics like Measure of Central Tendency, Probability Distribution Function, Null & Alternate Hypothesis, Linear & Logistic Regression, AUC-ROC Curve, etc can assist in training and testing models in Python or any other language, which can be useful for predictive analysis. To learn all the statistics topics, we can easily get the content on Udemy, Coursers, LinkedIn Forums, etc.

Let’s talk stats. No, not the kind you might encounter in your fantasy football league, but the kind that underpins every decision you’ll make in data analytics. Knowing the difference between a p-value and a t-test isn’t just for statisticians. According to LinkedIn's 2023 Emerging Jobs Report, statistical analysis is one of the top skills required for data analysts, with 76% of professionals listing it as a core competence. So, dust off those old college textbooks and get comfortable with terms like "regression analysis" and "ANOVA." Trust me, your future self will thank you.

I mentioned this recently and faced some criticism, but I'll emphasize it again: Excel is an essential tool to master in today's market for data analysis. Despite the buzz around advanced technologies, about 70% of companies rely on Excel for data tasks, according to Deloitte. Mastering Excel can open many doors for you, making it invaluable for data manipulation and analysis.

Não se prenda ao que o curso te propõe como desafio. Uma ótima forma de aprender é tentando resolver problemas. Se você for iniciante, não pense que você sabe, que é simples ou essa linha de raciocínio. Excel é uma ferramenta gigante. Não a toa ainda está no mercado e é muito útil em determinadas situações.

Before you groan, let me clarify. Excel is not just about creating colorful charts. It’s about harnessing a tool that, when used correctly, can perform complex data manipulations and analysis with ease. A study by the Harvard Business Review revealed that 81% of companies use Excel for critical business functions. Learning advanced Excel functions like PivotTables, VLOOKUP, and Power Query can set you apart in a sea of data enthusiasts.

Its advanced functions, pivot tables, and data visualization capabilities make it indispensable for both beginners and seasoned analysts. Excel continues to improve and outperform itself, maintaining its relevance in the rapidly changing landscape of data tools. By mastering Excel, you gain a significant asset that can streamline data manipulation and preliminary analysis, especially when time is of the essence. Practice with various datasets to sharpen your skills and fully leverage its capabilities.

As a data analyst, we cannot show the complex code written in the backend to show the desired result. For this, we should master Data Visualization skills and this can be achieved in Power BI or Tableau. If we are a beginner we can start by plotting basic visuals like line trends for monthly analysis, bar charts for comparison between the categories, and pie charts to show the percentage share of each category of the data. But at the same time, we should not make the dashboard look cluttered, it can reduce the interest of a user to gain insight from it. Once we master this skill we can start plotting complex visuals like heatmaps, and custom visuals, because this will give more insights than the basic visuals.

To acquire in-demand data visualization skills in data analytics, start by mastering tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn. Enroll in online courses from platforms like Coursera, edX, or Udacity to learn both the fundamentals and advanced techniques. Practice by working on real-world projects and datasets to build a strong portfolio. Stay updated with industry trends by following blogs, forums, and attending webinars. Developing a strong understanding of design principles and storytelling with data will enhance the effectiveness of your visualizations.

Ever heard the saying, "A picture is worth a thousand words?" In data analytics, a well-crafted visualization can be worth even more. Tools like Tableau, Power BI, and D3.js aren’t just fancy software; they’re essential for translating complex data into compelling stories. According to a survey by DataCamp, 64% of data professionals believe that data visualization skills are critical for advancing their careers . So, don’t just crunch the numbers—visualize them in ways that make your insights shine.

Muito bem colocado. Só acrescento o lembrete de que nada adianta aprender visualizações super diferentes se o seu público não consegue interpreta-la. Os dados têm que passar alguma mensagem nessa etapa. =)

The ability to communicate complex data findings is just as crucial as the analysis itself. Using data visualization tools like Tableau or Power BI, you can transform raw data into impactful charts and dashboards that tell a compelling story. Start with basic visualizations like bar charts and gradually explore more complex ones such as heat maps or network diagrams. Understanding the principles of good design and effective storytelling with data will enhance your ability to convey insights clearly and persuasively, making your analysis not only informative but also visually engaging.

Let's talk about the big guns—Big Data tools. Think Hadoop, Spark, and NoSQL databases. With data volumes growing at an unprecedented rate, traditional databases are often not up to the task. Gartner reports that the global Big Data market will reach $103 billion by 2027. Gaining proficiency in these tools not only makes you versatile but also highly valuable in any data-driven organization.

After learning sql implementation, we need to learn to handle data a little more efficiently. With having large volume of data, good performance becomes a necessity. Learn to deign your data warehouses the right way and optimize your queries.

Try to keep up with the trend. There is no particular set of skills you need to have to be future ready. The future is unprecedented so are the skills required to be future ready. I would suggest to look out for the articles and communities that actively engage in the data industry. They will know what's going on and what can help you to stay abreast of the industry. Don't rely on past data to be future ready.